In [241]:
import pandas as pd
import seaborn as sea
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
import numpy as np
import folium
from folium.plugins import MarkerCluster
In [221]:
globalFrame = pd.read_csv("trafficking.csv")
globalFrame = globalFrame.drop(globalFrame.columns[0],1)
globalFrame = globalFrame.drop(["Datasource","ageBroad","majorityStatus","majorityEntry"],1)
globalFrame = globalFrame.rename(columns = {"yearOfRegistration":"year","majorityStatusAtExploit":"majorityStatus","meansOfControlDebtBondage":"DebtBondage",
                                           "meansOfControlTakesEarnings":"EarningsStolen","meansOfControlRestrictsFinancialAccess": "WithholdsMoney",
                                           "meansOfControlThreats":"Threats","meansOfControlPsychologicalAbuse":"PsychologicalAbuse",
                                           "meansOfControlPhysicalAbuse":"PhysicalAbuse","meansOfControlSexualAbuse":"SexualAbuse",
                                           "meansOfControlFalsePromises":"FalsePromises","meansOfControlPsychoactiveSubstances":"PsychoactiveSubstances",
                                           "meansOfControlRestrictsMovement":"RestrictsMovement","meansOfControlRestrictsMedicalCare":"RestrictsMedicalCare",
                                           "meansOfControlExcessiveWorkingHours":"ExcessiveWorkingHours","meansOfControlUsesChildren":"UsesChildren",
                                           "meansOfControlThreatOfLawEnforcement":"ThreatOfLawEnforcement","meansOfControlWithholdsNecessities":"WithholdsNecessities",
                                           "meansOfControlWithholdsDocuments":"WithholdsDocuments","meansOfControlOther":"OtherControl","meansOfControlNotSpecified":"ControlNotSpecified",
                                           "recruiterRelationIntimatePartner":"IntimatePartner","recruiterRelationFriend":"Friend","recruiterRelationFamily":"Family",
                                           "recruiterRelationOther":"OtherRelation","recruiterRelationUnknown":"UnknownRelation"})
frame = globalFrame[globalFrame['citizenship'] == "US"]
frame = frame.reset_index()
frame = frame.drop('index',1)
frame.head()
frame["Cases"] = 1
frame.loc[frame['majorityStatus'] == '0', 'majorityStatus'] = "unkown"
ageFrame = frame.groupby(["majorityStatus","year"]).sum()
ageFrame['Cases'].plot.bar()
ageFrame = ageFrame.reset_index()
ageFrame

/opt/conda/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3145: DtypeWarning: Columns (6,55,57) have mixed types.Specify dtype option on import or set low_memory=False.
  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
Out[221]:
majorityStatus year DebtBondage EarningsStolen WithholdsMoney Threats PsychologicalAbuse PhysicalAbuse SexualAbuse FalsePromises ... typeOfSexPornography typeOfSexRemoteInteractiveServices typeOfSexPrivateSexualServices isAbduction IntimatePartner Friend Family OtherRelation UnknownRelation Cases
0 Adult 2015 5 12 0 13 20 15 4 4 ... 0 0 0 0 19 3 0 3 16 41
1 Adult 2016 12 21 8 45 47 45 29 20 ... 0 0 0 0 41 16 3 21 60 133
2 Adult 2017 10 20 1 63 50 51 21 9 ... 0 0 0 0 43 11 2 15 70 137
3 Adult 2018 5 16 2 37 38 28 11 8 ... 0 0 0 0 31 12 1 12 23 76
4 Minor 2015 13 36 4 56 101 72 42 10 ... 0 0 0 0 63 26 47 34 169 320
5 Minor 2016 21 43 6 111 140 118 76 20 ... 0 0 0 0 90 49 61 52 345 575
6 Minor 2017 20 44 5 109 149 104 56 18 ... 0 0 0 0 69 28 83 34 263 467
7 Minor 2018 6 17 3 67 107 70 65 3 ... 0 0 0 0 37 16 81 17 119 263
8 unkown 2015 13 18 2 39 56 46 11 4 ... 0 0 0 0 32 5 14 16 209 271
9 unkown 2016 13 40 12 80 114 119 29 15 ... 0 0 0 0 84 12 16 12 411 531
10 unkown 2017 27 36 6 122 107 107 37 7 ... 0 0 0 0 66 13 14 9 385 482
11 unkown 2018 11 27 3 81 98 96 41 11 ... 0 0 0 0 73 13 16 11 229 340

12 rows × 52 columns

In [211]:
ageFrame = ageFrame[ageFrame['majorityStatus'] != "unkown"]
physcialAbuse = sea.scatterplot(x = "year",y = "PhysicalAbuse",hue = "majorityStatus",data = ageFrame)
plt.show()
plt.clf()
sexualAbuse = sea.scatterplot(x = "year",y = "SexualAbuse",hue = "majorityStatus",data = ageFrame)
plt.show()
plt.clf()
pschologicalAbuse = sea.scatterplot(x = "year",y = "PsychologicalAbuse",hue = "majorityStatus",data = ageFrame)
plt.show()
plt.clf()
psychoactiveDrugs = sea.scatterplot(x = "year",y = "PsychoactiveSubstances",hue = "majorityStatus",data = ageFrame)
plt.show()
plt.clf()

<Figure size 432x288 with 0 Axes>
In [242]:
predictFrame = frame[frame["majorityStatus"] != "unkown"]
for index,row in predictFrame.iterrows():
    if(row.majorityStatus == "Minor"):
        predictFrame.loc[index,"BinaryAge"] = 1
    else:
        predictFrame.loc[index,"BinaryAge"] = 0
predictAge = predictFrame[["year","BinaryAge"]]
model = LogisticRegression()
model.fit(X = predictAge.drop("BinaryAge",1),y = predictAbuse["BinaryAge"])
model.predict(np.array([[2017]]))
predictFrame

/opt/conda/lib/python3.8/site-packages/pandas/core/indexing.py:1596: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self.obj[key] = _infer_fill_value(value)
/opt/conda/lib/python3.8/site-packages/pandas/core/indexing.py:1765: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  isetter(loc, value)
Out[242]:
year gender majorityStatus citizenship DebtBondage EarningsStolen WithholdsMoney Threats PsychologicalAbuse PhysicalAbuse ... isAbduction RecruiterRelationship CountryOfExploitation IntimatePartner Friend Family OtherRelation UnknownRelation Cases BinaryAge
0 2015 Female Minor US 0 0 0 0 0 0 ... 0 Intimate Partner US 1 0 0 0 0 1 1.0
1 2015 Female Adult US 0 0 0 0 1 0 ... 0 Not Specified US 0 0 0 0 1 1 0.0
2 2015 Female Minor US 0 0 0 1 0 1 ... 0 Friend/Acquaintance US 0 1 0 0 0 1 1.0
6 2015 Female Minor US 0 0 0 1 0 0 ... 0 Other US 0 0 0 1 0 1 1.0
7 2015 Female Minor US 1 1 0 0 1 1 ... 0 Intimate Partner US 1 0 0 0 0 1 1.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3631 2018 Male Minor US 0 0 0 0 1 0 ... 0 Family/Relative US 0 0 1 0 0 1 1.0
3632 2018 Male Minor US 0 0 0 1 1 1 ... 0 Family/Relative US 0 0 1 0 0 1 1.0
3633 2018 Male Minor US 0 0 0 1 1 1 ... 0 Family/Relative US 0 0 1 0 0 1 1.0
3634 2018 Male Minor US 0 0 0 0 0 0 ... 0 Family/Relative US 0 0 1 0 0 1 1.0
3635 2018 Male Minor US 0 0 0 0 1 1 ... 0 Family/Relative US 0 0 1 0 0 1 1.0

2012 rows × 61 columns

In [253]:
predictAbuse = predictFrame[["year","DebtBondage","EarningsStolen","WithholdsMoney","Threats","PhysicalAbuse","SexualAbuse","FalsePromises",
                            "PsychoactiveSubstances","RestrictsMovement","RestrictsMedicalCare","ExcessiveWorkingHours",
                            "UsesChildren","ThreatOfLawEnforcement","WithholdsNecessities","WithholdsDocuments",
                            "OtherControl","ControlNotSpecified","BinaryAge"]]
model = LinearRegression()
model.fit(X = predictAbuse.drop("BinaryAge",1),y = predictAbuse["BinaryAge"])
print("A victim of threats with 1 being a minor in 2017 is",model.predict(np.array([[2017,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]])))

A victim of threats with 1 being a minor in 2017 is [0.77239109]
In [251]:
predictRecruit = predictFrame[["year","IntimatePartner","Family","OtherRelation","UnknownRelation","BinaryAge"]]
model = LinearRegression()
model.fit(X = predictRecruit.drop("BinaryAge",1),y = predictAbuse["BinaryAge"])
print("A victim of a family member with one being a minor in 2017 is",model.predict(np.array([[2017,0,1,0,0]])))
print("A victim of an intimate partner with one being a minor in 2015 is",model.predict(np.array([[2015,1,0,0,0]])))

A victim of a family member with one being a minor in 2017 is [0.96826561]
A victim of an intimate partner with one being a minor in 2015 is [0.71835606]
In [248]:
m = folium.Map()
cluster = MarkerCluster().add_to(m)
for index,row in globalFrame.iterrows():
    if(row.citizenship == "CO"):
        folium.map.Marker(location = ['4.5709','-74.2973'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "MD"):
        folium.map.Marker(location = ['47.4116','28.3699'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "RO"):
        folium.map.Marker(location = ['45.9432','24.9668'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "UA"):
        folium.map.Marker(location = ['48.3794','31.1656'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "BY"):
        folium.map.Marker(location = ['53.7098','27.9534'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "HT"):
        folium.map.Marker(location = ['18.9712','-72.2852'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "UZ"):
        folium.map.Marker(location = ['41.3775','64.5853'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "LK"):
        folium.map.Marker(location = ['7.8731','80.7718'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "LK"):
        folium.map.Marker(location = ['7.8731','80.7718'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "MM"):
        folium.map.Marker(location = ['21.9162','95.9560'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "UG"):
        folium.map.Marker(location = ['1.3733','32.2903'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "ID"):
        folium.map.Marker(location = ['-0.7893','113.9213'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "ID"):
        folium.map.Marker(location = ['-0.7893','113.9213'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "KG"):
        folium.map.Marker(location = ['42.882004','74.582748'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "AF"):
        folium.map.Marker(location = ['33.9391','67.7100'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "ER"):
        folium.map.Marker(location = ['15.1794','39.7823'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "NG"):
        folium.map.Marker(location = ['17.6078','8.0817'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "NP"):
        folium.map.Marker(location = ['28.3949','84.1240'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "PH"):
        folium.map.Marker(location = ['12.8797','121.7740'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "KH"):
        folium.map.Marker(location = ['12.5657','104.9910'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "BD"):
        folium.map.Marker(location = ['23.6850','90.3563'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "US"):
        folium.map.Marker(location = ['37.0902','-95.7129'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "TH"):
        folium.map.Marker(location = ['15.8700','100.9925'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "VN"):
        folium.map.Marker(location = ['14.0583','108.2772'],popup = row.citizenship).add_to(cluster)
m
Out[248]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [ ]: